1. Complete Your 12th Standard with a Strong Background in Math and Science To complete your 12th standard with a strong background in math and science, specifically to pursue a career as a data scientist, you should focus on building a solid foundation in relevant subjects. Here’s a tailored approach: Focus on subjects like Mathematics, Science Subjects, Statistics and Probability, Computer Science, and any other relevant subjects during your 12th standard. 2. Earn a Bachelor’s Degree in a Relevant Field Pursue a bachelor’s degree in Mathematics, Statistics, Computer Science, Data Science, Engineering, Economics, or any other quantitative discipline. While a specific degree is not mandatory to become a data scientist, a strong foundation in quantitative subjects is essential. 3. Acquire Skills in Programming and Data Analysis Learn programming languages commonly used in data science such as Python, R, SQL, and/or Java. Gain proficiency in data analysis, data visualization, and manipulation techniques. Familiarize yourself with tools and libraries used in data science such as Pandas, NumPy, Scikit-learn, TensorFlow, and Matplotlib. 4. Gain Knowledge in Machine Learning and Statistics Study machine learning algorithms, techniques, and methodologies used in data analysis and predictive modeling. Learn statistical methods and concepts relevant to data analysis, hypothesis testing, and experimental design. 5. Complete Advanced Education (Optional) Consider pursuing a master’s degree or Ph.D. in Data Science, Statistics, Computer Science, or a related field for advanced training and specialization. Advanced degrees can provide in-depth knowledge, research opportunities, and networking opportunities in data science. 6. Build a Portfolio of Projects Work on real-world projects involving data analysis, machine learning, and predictive modeling. Showcase your projects on platforms like GitHub or Kaggle to demonstrate your skills and expertise to potential employers. 7. Gain Practical Experience through Internships or Entry-level Positions Look for internships or entry-level positions in data-related roles such as data analyst, business analyst, or research assistant. Gain hands-on experience working with real data, solving problems, and collaborating with multidisciplinary teams. 8. Continuous Learning and Skill Development Stay updated with the latest trends, techniques, and tools in data science through online courses, workshops, conferences, and professional certifications. Participate in online communities, forums, and meetups to connect with fellow data scientists and experts in the field. 9. Network and Build Professional Relationships Attend industry events, conferences, and networking events to meet professionals in the field and learn about job opportunities. Connect with data scientists, recruiters, and professionals on professional networking platforms like LinkedIn. 10. Apply for Data Scientist Positions Once you have acquired the necessary skills and experience, start applying for data scientist positions in companies, research institutions, consulting firms, and startups. Tailor your resume and cover letter to highlight your relevant skills, experience, and projects. Prepare for technical interviews by practicing coding challenges, machine learning algorithms, and case studies. Becoming a data scientist is a journey that requires continuous learning, practical experience, and dedication. By following the steps outlined above and staying committed to your goals, you can embark on a rewarding career in data science. i think this will be helpful for students